mcp-server-mas-sequential-thinking

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Basic Information

This repository provides a Python-based server that implements a Sequential Thinking Multi-Agent System (MAS) and exposes it as an MCP tool named sequentialthinking. It is built with the Agno MAS framework and served to MCP clients via a stdio executable so external LLMs can iteratively drive complex problem-solving workflows. The server coordinates a Team (Coordinator) agent and multiple specialist agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to actively process, analyze, synthesize, and revise thought steps rather than merely logging them. It includes a Pydantic ThoughtData schema for validated inputs, optional integration with external research tools like Exa, and supports multiple LLM providers via environment configuration. The project targets developers and integrators who want to run an orchestrated, role-based thinking process within MCP-compatible clients and contains installation, configuration, and usage instructions for local testing and deployment.

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App Details

Features
The server uses a true MAS architecture where a Coordinator (Team) delegates sub-tasks to specialized agents for distributed processing and synthesis. Inputs follow a Pydantic ThoughtData model for strict validation and structured tool calls. The system supports revisions and branching of thought sequences and returns a detailed JSON response with coordinatorResponse, branch metadata, thought history, and status. It integrates with external tooling for research via Exa and supports multiple LLM providers (DeepSeek, Groq, OpenRouter, Kimi, Ollama) configurable by environment variables. Logging is robust with rotating file handlers and console output. The project includes installation paths via Smithery or manual setup, virtual environment recommendations, example MCP client env configuration, and local testing instructions using MCP Inspector.
Use Cases
This repository helps developers and teams build richer, higher-quality reasoning pipelines by replacing single-agent logging approaches with coordinated multi-role processing. The Coordinator synthesizes specialist outputs to provide actionable guidance, recommend revisions, and explore alternative branches, enabling more nuanced and iterative analysis driven by external LLMs. Pydantic validation and structured JSON responses make integration with MCP clients predictable and programmatic. Support for multiple LLM providers and optional research tooling allows teams to balance capability, cost, and external information needs. Detailed logging and local testing instructions simplify debugging and validation. Overall, it is useful for anyone needing an orchestrated sequential thinking tool that emphasizes depth of analysis and structured iteration rather than token efficiency.

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